Triple
T15548721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Eisenhardt |
E370681
|
entity |
| Predicate | edited |
P1932
|
FINISHED |
| Object | Meru |
E318870
|
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: Meru | Statement: [Bob Eisenhardt, edited, Meru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meru Context triple: [Bob Eisenhardt, edited, Meru]
-
A.
Meru
chosen
Meru is a town in eastern Kenya that serves as a commercial and administrative hub for the surrounding agricultural region near Mount Kenya.
-
B.
Mount Meru
Mount Meru is a dormant stratovolcano in northern Tanzania, renowned as one of Africa’s highest peaks and a prominent feature near Arusha and Mount Kilimanjaro.
-
C.
Mount Meru
Mount Meru is the sacred, cosmic mountain at the center of the universe in Hindu, Buddhist, and Jain cosmology, serving as the axis mundi and abode of gods.
-
D.
Meru Peak
Meru Peak is a prominent and technically challenging Himalayan mountain in the Indian Garhwal region, renowned among climbers for its steep granite faces and difficult ascents.
-
E.
Gigiri
Gigiri is an affluent diplomatic and residential district in Nairobi, Kenya, known for hosting major international institutions and embassies.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a93121881909d88ca55a39252ac |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff56bdbca08190b5eb541c5eb4bb09 |
completed | May 9, 2026, 3:46 p.m. |
Created at: April 10, 2026, 4:08 a.m.