Triple
T834971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love (novel) |
E18049
|
entity |
| Predicate | centralCharacter |
P9202
|
FINISHED |
| Object | May |
E74874
|
NE FINISHED |
How this triple was built (2 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: May | Statement: [Love (novel), centralCharacter, May]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: May Context triple: [Love (novel), centralCharacter, May]
-
A.
May
chosen
May is a common English surname borne by numerous individuals, including former UK Prime Minister Theresa May.
-
B.
Maio
Maio is one of the main islands of Cape Verde, known for its quiet beaches, salt flats, and relatively flat, arid landscape.
-
C.
April
April is a spring month in the Gregorian calendar often associated with mild weather and the blooming of many flowers.
-
D.
June
June is an early-summer month in the Northern Hemisphere often associated with favorable weather for outdoor activities and mountaineering.
-
E.
September
September is the ninth month of the year in the Gregorian calendar, marking the transition from summer to autumn in the Northern Hemisphere and from winter to spring in the Southern Hemisphere.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2b66c908190a52f731119b77a1e |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d9b57fc8190981ed4eeb2ac5548 |
completed | March 3, 2026, 11:24 p.m. |
Created at: March 1, 2026, 7:38 p.m.