Triple
T5934153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Veolia Environnement |
E132003
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object |
VIE
VIE is the stock ticker symbol for Veolia Environnement, a French multinational company specializing in water, waste, and energy management services.
|
E556357
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: VIE | Statement: [Veolia Environnement, tickerSymbol, VIE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VIE Context triple: [Veolia Environnement, tickerSymbol, VIE]
-
A.
VIE
VIE is the three-letter IATA airport code for Vienna International Airport, the main international gateway to Vienna, Austria.
-
B.
VIV
VIV is the ICAO airline designator assigned to Viva Aerobus, a Mexican low-cost carrier.
-
C.
VIBN
VIBN is the ICAO airport code for Lal Bahadur Shastri International Airport serving Varanasi, India.
-
D.
VNU
VNU is a leading public research university system in Vietnam, headquartered in Hanoi and known for its comprehensive programs and high academic standards.
-
E.
Vives Network
Vives Network is a collaborative association of universities and higher education institutions in the Catalan-speaking regions that promotes academic, cultural, and research cooperation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: VIE Triple: [Veolia Environnement, tickerSymbol, VIE]
Generated description
VIE is the stock ticker symbol for Veolia Environnement, a French multinational company specializing in water, waste, and energy management services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: VIE Target entity description: VIE is the stock ticker symbol for Veolia Environnement, a French multinational company specializing in water, waste, and energy management services.
-
A.
VIE
VIE is the three-letter IATA airport code for Vienna International Airport, the main international gateway to Vienna, Austria.
-
B.
VIV
VIV is the ICAO airline designator assigned to Viva Aerobus, a Mexican low-cost carrier.
-
C.
VIBN
VIBN is the ICAO airport code for Lal Bahadur Shastri International Airport serving Varanasi, India.
-
D.
VNU
VNU is a leading public research university system in Vietnam, headquartered in Hanoi and known for its comprehensive programs and high academic standards.
-
E.
Vives Network
Vives Network is a collaborative association of universities and higher education institutions in the Catalan-speaking regions that promotes academic, cultural, and research cooperation.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c038a0c4e481908170d615330edb1a |
completed | March 22, 2026, 6:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c069e450819096b268637ffcd219 |
completed | March 23, 2026, 4:24 a.m. |
| NEDg | Description generation | batch_69c0c46fabf081908484ba066c25187b |
completed | March 23, 2026, 4:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0c4f21528819093a36c07e2637446 |
completed | March 23, 2026, 4:43 a.m. |
Created at: March 22, 2026, 4 p.m.